项目：

大数据问题中特征根与特征向量的理论及其应用 2021-2023 No. 12001518  青年科学基金项目 主持

入选国家创新人才计划青年项目

研究方向：大维随机矩阵；高维统计推断； 

[1] X. Han, G. Pan and B. Zhang. The Tracy-Widom law for the Largest Eigenvalue of
F Type Matrix. The Annals of Statistics, vol.44, no. 4, pp. 1564–1592, 2016.

[2] J. Gao, X. Han, G. Pan and Y. R. Yang. High Dimensional Correlation Matrices:
CLT and Its Applications. Journal of the Royal Statistical Society: Series B (Statistical
Methodology), vol. 79, no. 3, pp. 677–693, 2017.

[3] X. Han, G. Pan and Q. Yang. A Unified Matrix Model including Both CCA and F
Matrices in Multivariate Analysis: The Largest Eigenvalue and Its Applications.
Bernoulli, vol. 24, no. 4B, pp. 3447–3468, 2018.

[4] X. Wang, X. Han and G. Pan. The Logarithmic Law of Sample Covariance Matrices
Near Singularity. Bernoulli, vol. 24, no. 1, pp. 80–114, 2018.

[5] T. Cai, X. Han and G. Pan. Limiting Laws for Divergent Spiked Eigenvalues and
Largest Non-spiked Eigenvalue of Sample Covariance Matrices. The Annals of Statistics,
vol. 48, no. 3, pp. 1255-1280. 2020.

[6] Q. Fan, X. Han, B. Jiang and G. Pan. Estimating a Large System of Seemingly
Unrelated Regressions Using Penalized Quasi-Maximum Likelihood Estimation.
Econometric Theory, vol. 36, no. 3, pp. 526-558, 2020.

[7] J. Fan, Y. Fan, X. Han, and J. Lv. Asymptotic theory of eigenvectors for random
matrices with diverging spikes. Journal of the American Statistical Association, vol. 177,
no 538, pp. 996-1009, 2022.

[8] J. Fan, Y. Fan, X. Han, and J. Lv. Simple: Statistical inference on member ship
profiles in large networks. Journal of the Royal Statistical Society Series B(Statistical
Methodology), vol. 84, no. 2, pp. 630–653, 2022.

[9] X. Han, X. Tong, and Y. Fan. Eigen selection in spectral clustering: a theory guided
practice. Journal of the American Statistical Association, accepted.

[10] X. Han, Q. Yang, and Y. Fan. Universal Rank Inference via Residual Sampling with

 application to large networks. The Annals of Statistics, accepted.